Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data

Liu, Yaning; Pau, George Shu Heng; Finsterle, Stefan

doi:10.1016/j.cageo.2017.04.001

Title: Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data

Authors:: Liu, Yaning; Pau, George Shu Heng; Finsterle, Stefan

Publication Date:: Wed Nov 01 00:00:00 EDT 2017

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:: 1550490

Grant/Contract Number:: DEAC02-05CH11231

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Computers and Geosciences

Additional Journal Information:: Journal Name: Computers and Geosciences Journal Volume: 108 Journal Issue: C; Journal ID: ISSN 0098-3004

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Liu, Yaning, Pau, George Shu Heng, and Finsterle, Stefan. Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data.  United Kingdom: N. p., 2017. 
Web.  doi:10.1016/j.cageo.2017.04.001.

Copy to clipboard


                    Liu, Yaning, Pau, George Shu Heng, & Finsterle, Stefan. Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data.  United Kingdom.  https://doi.org/10.1016/j.cageo.2017.04.001

Copy to clipboard


                    Liu, Yaning, Pau, George Shu Heng, and Finsterle, Stefan. Wed .  
"Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data".  United Kingdom.  https://doi.org/10.1016/j.cageo.2017.04.001.

Copy to clipboard


                    
@article{osti_1550490,

  title        = {Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data},

  author       = {Liu, Yaning and Pau, George Shu Heng and Finsterle, Stefan},

  abstractNote = {},

  doi          = {10.1016/j.cageo.2017.04.001},

  journal      = {Computers and Geosciences},

  number       = C,

  volume       = 108,

  place        = {United Kingdom},

  year         = {Wed Nov 01 00:00:00 EDT 2017},

  month        = {Wed Nov 01 00:00:00 EDT 2017}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Publisher's Version of Record
https://doi.org/10.1016/j.cageo.2017.04.001

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 5 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Ensemble samplers with affine invariance
journal, January 2010

Goodman, Jonathan; Weare, Jonathan
Communications in Applied Mathematics and Computational Science, Vol. 5, Issue 1
DOI: 10.2140/camcos.2010.5.65

Joint Hydrological-Geophysical Inversion for Soil Structure Identification
journal, February 2008

Finsterle, Stefan; Kowalsky, Michael B.
Vadose Zone Journal, Vol. 7, Issue 1
DOI: 10.2136/vzj2006.0078

Inverse Problem Theory and Methods for Model Parameter Estimation
book, January 2005

Tarantola, Albert
DOI: 10.1137/1.9780898717921

Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation
journal, January 2012

Morzfeld, M.; Chorin, A. J.
Nonlinear Processes in Geophysics, Vol. 19, Issue 3
DOI: 10.5194/npg-19-365-2012

Efficient input—output model representations
journal, March 1999

Rabitz, Herschel; Aliş, Ömer F.; Shorter, Jeffrey
Computer Physics Communications, Vol. 117, Issue 1-2
DOI: 10.1016/S0010-4655(98)00152-0

Robust estimation of hydrogeologic model parameters
journal, November 1998

Finsterle, Stefan; Najita, Julie
Water Resources Research, Vol. 34, Issue 11
DOI: 10.1029/98WR02174

Definitions and examples of inverse and ill-posed problems
journal, January 2008

Kabanikhin, S. I.
Journal of Inverse and Ill-posed Problems, Vol. 16, Issue 4
DOI: 10.1515/JIIP.2008.019

Sequential Monte Carlo Methods for Dynamic Systems
journal, September 1998

Liu, Jun S.; Chen, Rong
Journal of the American Statistical Association, Vol. 93, Issue 443
DOI: 10.1080/01621459.1998.10473765

Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments
journal, March 1995

RamaRao, Banda S.; LaVenue, A. Marsh; De Marsily, Ghislain
Water Resources Research, Vol. 31, Issue 3
DOI: 10.1029/94WR02258

A random map implementation of implicit filters
journal, February 2012

Morzfeld, Matthias; Tu, Xuemin; Atkins, Ethan
Journal of Computational Physics, Vol. 231, Issue 4
DOI: 10.1016/j.jcp.2011.11.022

Inversion algorithms for large-scale geophysical electromagnetic measurements
journal, December 2009

Abubakar, A.; Habashy, T. M.; Li, M.
Inverse Problems, Vol. 25, Issue 12
DOI: 10.1088/0266-5611/25/12/123012

A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion
journal, January 2012

Martin, James; Wilcox, Lucas C.; Burstedde, Carsten
SIAM Journal on Scientific Computing, Vol. 34, Issue 3
DOI: 10.1137/110845598

Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling
journal, January 2009

Vrugt, J. A.; ter Braak, C. J. F.; Diks, C. G. H.
International Journal of Nonlinear Sciences and Numerical Simulation, Vol. 10, Issue 3
DOI: 10.1515/IJNSNS.2009.10.3.273

Reduced order modeling in iTOUGH2
journal, April 2014

Pau, George Shu Heng; Zhang, Yingqi; Finsterle, Stefan
Computers & Geosciences, Vol. 65
DOI: 10.1016/j.cageo.2013.08.008

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
journal, January 2002

Xiu, Dongbin; Karniadakis, George Em
SIAM Journal on Scientific Computing, Vol. 24, Issue 2
DOI: 10.1137/S1064827501387826

Bayesian Compressive Sensing Using Laplace Priors
journal, January 2010

Babacan, S. D.; Molina, R.; Katsaggelos, A. K.
IEEE Transactions on Image Processing, Vol. 19, Issue 1
DOI: 10.1109/TIP.2009.2032894

Implicit sampling for particle filters
journal, September 2009

Chorin, A. J.; Tu, X.
Proceedings of the National Academy of Sciences, Vol. 106, Issue 41
DOI: 10.1073/pnas.0909196106

Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory
journal, December 1997

Jaime Gómez-Hernánez, J.; Sahuquillo, Andrés; Capilla, JoséE.
Journal of Hydrology, Vol. 203, Issue 1-4
DOI: 10.1016/S0022-1694(97)00098-X

A Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a Polygonal Tundra Site
journal, January 2016

Liu, Yaning; Bisht, Gautam; Subin, Zachary M.
Vadose Zone Journal, Vol. 15, Issue 2
DOI: 10.2136/vzj2015.05.0068

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
journal, January 2002

Arulampalam, M. S.; Maskell, S.; Gordon, N.
IEEE Transactions on Signal Processing, Vol. 50, Issue 2
DOI: 10.1109/78.978374

Dimensionality Reduction for Complex Models via Bayesian Compressive Sensing
journal, January 2014

Sargsyan, Khachik; Safta, Cosmin; Najm, Habib N.
International Journal for Uncertainty Quantification, Vol. 4, Issue 1
DOI: 10.1615/Int.J.UncertaintyQuantification.2013006821

`emcee` : The MCMC Hammer
journal, March 2013

Foreman-Mackey, Daniel; Hogg, David W.; Lang, Dustin
Publications of the Astronomical Society of the Pacific, Vol. 125, Issue 925
DOI: 10.1086/670067

Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods
journal, March 2010

Mondal, A.; Efendiev, Y.; Mallick, B.
Advances in Water Resources, Vol. 33, Issue 3
DOI: 10.1016/j.advwatres.2009.10.010

Parameter estimation by implicit sampling
journal, January 2015

Morzfeld, Matthias; Tu, Xuemin; Wilkening, Jon
Communications in Applied Mathematics and Computational Science, Vol. 10, Issue 2
DOI: 10.2140/camcos.2015.10.205

Accurate construction of high dimensional model representation with applications to uncertainty quantification
journal, August 2016

Liu, Yaning; Yousuff Hussaini, M.; Ökten, Giray
Reliability Engineering & System Safety, Vol. 152
DOI: 10.1016/j.ress.2016.03.021

Implicit Particle Methods and Their Connection with Variational Data Assimilation
journal, June 2013

Atkins, Ethan; Morzfeld, Matthias; Chorin, Alexandre J.
Monthly Weather Review, Vol. 141, Issue 6
DOI: 10.1175/MWR-D-12-00145.1

An Introduction to MCMC for Machine Learning
journal, January 2003

Andrieu, Christophe; de Freitas, Nando; Doucet, Arnaud
Machine Learning, Vol. 50, Issue 1/2, p. 5-43
DOI: 10.1023/A:1020281327116

Review of surrogate modeling in water resources: REVIEW
journal, July 2012

Razavi, Saman; Tolson, Bryan A.; Burn, Donald H.
Water Resources Research, Vol. 48, Issue 7
DOI: 10.1029/2011WR011527

On importance sampling Monte Carlo approach to uncertainty analysis for flow and transport in porous media
journal, November 2003

Lu, Zhiming; Zhang, Dongxiao
Advances in Water Resources, Vol. 26, Issue 11
DOI: 10.1016/S0309-1708(03)00106-4

Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information
journal, February 1986

Carrera, Jesus; Neuman, Shlomo P.
Water Resources Research, Vol. 22, Issue 2
DOI: 10.1029/WR022i002p00199

Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework: HYDROLOGIC DATA ASSIMILATION
journal, July 2007

Liu, Yuqiong; Gupta, Hoshin V.
Water Resources Research, Vol. 43, Issue 7
DOI: 10.1029/2006WR005756

Implicit particle filters for data assimilation
journal, January 2010

Chorin, Alexandre; Morzfeld, Matthias; Tu, Xuemin
Communications in Applied Mathematics and Computational Science, Vol. 5, Issue 2
DOI: 10.2140/camcos.2010.5.221

A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1
journal, January 1980

van Genuchten, M. Th.
Soil Science Society of America Journal, Vol. 44, Issue 5
DOI: 10.2136/sssaj1980.03615995004400050002x

Evaluating two sparse grid surrogates and two adaptation criteria for groundwater Bayesian uncertainty quantification
journal, April 2016

Zeng, Xiankui; Ye, Ming; Burkardt, John
Journal of Hydrology, Vol. 535
DOI: 10.1016/j.jhydrol.2016.01.058

Computational Methods for Inverse Problems
book, January 2002

Vogel, Curtis R.
Frontiers in Applied Mathematics
DOI: 10.1137/1.9780898717570

Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods
journal, January 2015

Liu, Yaning; Jimenez, Edwin; Hussaini, M. Yousuff
International Journal of Wildland Fire, Vol. 24, Issue 3
DOI: 10.1071/WF13097

A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors
journal, October 2010

Schoups, Gerrit; Vrugt, Jasper A.
Water Resources Research, Vol. 46, Issue 10
DOI: 10.1029/2009WR008933

Capillary Conduction of Liquids Through Porous Mediums
journal, November 1931

Richards, L. A.
Physics, Vol. 1, Issue 5
DOI: 10.1063/1.1745010

Similar Records in DOE PAGES and OSTI.GOV collections:

Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data

Journal Article Liu, Yaning ; Pau, George Shu Heng ; Finsterle, Stefan - Computers and Geosciences

Bayesian inverse modeling techniques are computationally expensive because many forward simulations are needed when sampling the posterior distribution of the parameters. In this paper, we combine the implicit sampling method and generalized polynomial chaos expansion (gPCE) to significantly reduce the computational cost of performing Bayesian inverse modeling. There are three steps in this approach: (1) find the maximizer of the likelihood function using deterministic approaches; (2) construct a gPCE-based surrogate model using the results from a limited number of forward simulations; and (3) efficiently sample the posterior distribution of the parameters using implicit sampling method. The cost of constructing themore »« less
https://doi.org/10.1016/j.cageo.2017.04.001
A Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a Polygonal Tundra Site

Journal Article Liu, Yaning ; Bisht, Gautam ; Subin, Zachary M. ; ... - Vadose Zone Journal

High‐resolution predictions of land surface hydrological dynamics are desirable for improved investigations of regional‐ and watershed‐scale processes. Direct deterministic simulations of fine‐resolution land surface variables present many challenges, including high computational cost. We therefore propose the use of reduced‐order modeling techniques to facilitate emulation of fine‐resolution simulations. We use an emulator, Gaussian process regression, to approximate fine‐resolution four‐dimensional soil moisture fields predicted using a three‐dimensional surface‐subsurface hydrological simulator (PFLOTRAN). A dimension‐reduction technique known as “proper orthogonal decomposition” is further used to improve the efficiency of the resulting reduced‐order model (ROM). The ROM reduces simulation computational demand to negligible levels comparedmore »« less
Cited by 9
https://doi.org/10.2136/vzj2015.05.0068

Full Text Available
A Hybrid Hydrologic-Geophysical Inverse Technique for the Assessment and Monitoring of Leachates in the Vadose Zone

Technical Report ALUMBAUGH, DAVID L ; YEH, JIM ; LABRECQUE, DOUG ; ...

The objective of this study is to develop and field test a new, integrated Hybrid Hydrologic-Geophysical Inverse Technique (HHGIT) for characterization of the vadose zone at contaminated sites. This new approach to site characterization and monitoring can provide detailed maps of hydrogeological heterogeneity and the extent of contamination by combining information from 3D electric resistivity tomography (ERT) and/or 2D cross borehole ground penetrating radar (XBGPR) surveys, statistical information about heterogeneity and hydrologic processes, and sparse hydrologic data. Because the electrical conductivity and dielectric constant of the vadose zone (from the ERT and XBGPR measurements, respectively) can be correlated to themore »« less
https://doi.org/10.2172/14908

Full Text Available
A hybrid hydrologic-geophysical inverse technique for the assessment and monitoring of leachates in the vadose zone. 1997 annual progress report

Technical Report Alumbaugh, D L

'It is the objective of this proposed study to develop and field test a new, integrated Hybrid Hydrologic-Geophysical Inverse Technique (HHGIT) for characterization of the vadose zone at contaminated sites. This fundamentally new approach to site characterization and monitoring will provide detailed knowledge about hydrological properties, geological heterogeneity and the extent and movement of contamination. HHGIT combines electrical resistivity tomography (ERT) to geophysically sense a 3D volume, statistical information about fabric of geological formations, and sparse data on moisture and contaminant distributions. Combining these three types of information into a single inversion process will provide much better estimates of spatiallymore »« less
https://doi.org/10.2172/13568

Full Text Available
A hybrid hydrologic-geophysical inverse technique for the assessment and monitoring of leachates in the vadose zone. 1998 annual progress report

Technical Report Alumbaugh, D L ; Glass, R J ; Yeh, T C ; ...

'The objective of this study is to develop and field test a new, integrated Hybrid Hydrologic-Geophysical Inverse Technique (HHGIT) for characterization of the vadose zone at contaminated sites. This new approach to site characterization and monitoring can provide detailed maps of hydrogeological heterogeneity and the extent of contamination by combining information from electric resistivity tomography (ERT) surveys, statistical information about heterogeneity and hydrologic processes, and sparse hydrologic data. Because the electrical conductivity of the vadose zone (from the ERT measurements) can be correlated to the fluid saturation and/or contaminant concentration, the hydrologic and geophysical measurements are related. As of themore »« less
https://doi.org/10.2172/13569

Full Text Available

Similar Records

Title: Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data

Citation Formats

Ensemble samplers with affine invariance journal, January 2010

Joint Hydrological-Geophysical Inversion for Soil Structure Identification journal, February 2008

Inverse Problem Theory and Methods for Model Parameter Estimation book, January 2005

Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation journal, January 2012

Efficient input—output model representations journal, March 1999

Robust estimation of hydrogeologic model parameters journal, November 1998

Definitions and examples of inverse and ill-posed problems journal, January 2008

Sequential Monte Carlo Methods for Dynamic Systems journal, September 1998

Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments journal, March 1995

A random map implementation of implicit filters journal, February 2012

Inversion algorithms for large-scale geophysical electromagnetic measurements journal, December 2009

A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion journal, January 2012

Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling journal, January 2009

Reduced order modeling in iTOUGH2 journal, April 2014

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations journal, January 2002

Bayesian Compressive Sensing Using Laplace Priors journal, January 2010

Implicit sampling for particle filters journal, September 2009

Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory journal, December 1997

A Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a Polygonal Tundra Site journal, January 2016

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking journal, January 2002

Dimensionality Reduction for Complex Models via Bayesian Compressive Sensing journal, January 2014

emcee : The MCMC Hammer journal, March 2013

Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods journal, March 2010

Parameter estimation by implicit sampling journal, January 2015

Accurate construction of high dimensional model representation with applications to uncertainty quantification journal, August 2016

Implicit Particle Methods and Their Connection with Variational Data Assimilation journal, June 2013

An Introduction to MCMC for Machine Learning journal, January 2003

Review of surrogate modeling in water resources: REVIEW journal, July 2012

On importance sampling Monte Carlo approach to uncertainty analysis for flow and transport in porous media journal, November 2003

Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information journal, February 1986

Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework: HYDROLOGIC DATA ASSIMILATION journal, July 2007

Implicit particle filters for data assimilation journal, January 2010

A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1 journal, January 1980

Evaluating two sparse grid surrogates and two adaptation criteria for groundwater Bayesian uncertainty quantification journal, April 2016

Computational Methods for Inverse Problems book, January 2002

Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods journal, January 2015

A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors journal, October 2010

Capillary Conduction of Liquids Through Porous Mediums journal, November 1931

Ensemble samplers with affine invariance
journal, January 2010

Joint Hydrological-Geophysical Inversion for Soil Structure Identification
journal, February 2008

Inverse Problem Theory and Methods for Model Parameter Estimation
book, January 2005

Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation
journal, January 2012

Efficient input—output model representations
journal, March 1999

Robust estimation of hydrogeologic model parameters
journal, November 1998

Definitions and examples of inverse and ill-posed problems
journal, January 2008

Sequential Monte Carlo Methods for Dynamic Systems
journal, September 1998

Pilot Point Methodology for Automated Calibration of an Ensemble of conditionally Simulated Transmissivity Fields: 1. Theory and Computational Experiments
journal, March 1995

A random map implementation of implicit filters
journal, February 2012

Inversion algorithms for large-scale geophysical electromagnetic measurements
journal, December 2009

A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion
journal, January 2012

Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling
journal, January 2009

Reduced order modeling in iTOUGH2
journal, April 2014

The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
journal, January 2002

Bayesian Compressive Sensing Using Laplace Priors
journal, January 2010

Implicit sampling for particle filters
journal, September 2009

Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory
journal, December 1997

A Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a Polygonal Tundra Site
journal, January 2016

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
journal, January 2002

Dimensionality Reduction for Complex Models via Bayesian Compressive Sensing
journal, January 2014

`emcee` : The MCMC Hammer
journal, March 2013

Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods
journal, March 2010

Parameter estimation by implicit sampling
journal, January 2015

Accurate construction of high dimensional model representation with applications to uncertainty quantification
journal, August 2016

Implicit Particle Methods and Their Connection with Variational Data Assimilation
journal, June 2013

An Introduction to MCMC for Machine Learning
journal, January 2003

Review of surrogate modeling in water resources: REVIEW
journal, July 2012

On importance sampling Monte Carlo approach to uncertainty analysis for flow and transport in porous media
journal, November 2003

Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information
journal, February 1986

Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework: HYDROLOGIC DATA ASSIMILATION
journal, July 2007

Implicit particle filters for data assimilation
journal, January 2010

A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1
journal, January 1980

Evaluating two sparse grid surrogates and two adaptation criteria for groundwater Bayesian uncertainty quantification
journal, April 2016

Computational Methods for Inverse Problems
book, January 2002

Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods
journal, January 2015

A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors
journal, October 2010

Capillary Conduction of Liquids Through Porous Mediums
journal, November 1931